Helical toroid phantom for 3D flow imaging investigations
Bibliographic record
Abstract
Abstract The medical physics community has hitherto lacked an effective calibration phantom to holistically evaluate the performance of three-dimensional (3D) flow imaging techniques. Here, we present the design of a new omnidirectional, three-component (3-C) flow phantom whose lumen is consisted of a helical toroid structure (4 mm lumen diameter; helically winded for 5 revolutions over a torus with 10 mm radius; 5 mm helix radius). This phantom’s intraluminal flow trajectory embraces all combinations of x , y , and z directional components, as confirmed using computational fluid dynamics (CFD) simulations. The phantom was physically fabricated via lost-core casting with polyvinyl alcohol cryogel (PVA) as the tissue mimic. 3D ultrasound confirmed that the phantom lumen expectedly resembled a helical toroid geometry. Pulsed Doppler measurements showed that the phantom, when operating under steady flow conditions (3 ml s −1 flow rate), yielded flow velocity magnitudes that agreed well with those derived from CFD at both the inner torus (−47.6 ± 5.7 versus −52.0 ± 2.2 cm s −1 ; mean ± 1 S.D.) and the outer torus (49.5 ± 4.2 versus 48.0 ± 1.7 cm s −1 ). Additionally, 3-C velocity vectors acquired from multi-angle pulsed Doppler showed good agreement with CFD-derived velocity vectors (<7% and 10° difference in magnitude and flow angle, respectively). Ultrasound color flow imaging further revealed that the phantom’s axial flow pattern was aligned with the CFD-derived flow profile. Overall, the helical toroid phantom has strong potential as an investigative tool in 3D flow imaging innovation endeavors, such as the development of flow vector estimators and visualization algorithms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".